Comments (1)
Valid observation. With ga_retinanet, we faced some issues during optimization. We did not try a thorough grid search of the hyper parameters, but a few lrs and steps. This seem to work reasonably well in this case and we are still experimenting over hyper parameters. You can try to play around and feel free to let us know, if you find something better. The AP reported in the README.md is produces using this config. Regarding the batch size with linear scaling, in our experience, overall for all models, it did effect but not significantly, to give you an idea, may be around 0.5-1.2% MR points in some cases.
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Related Issues (20)
- Impact of mean_teacher on the training process HOT 1
- CSP pretrained weights HOT 1
- Reproduce resutls on Caltech dataset HOT 3
- Information regarding the training HOT 1
- Image_scale of Caltech while training HOT 6
- ImportError: /pedestron/tools/../mmdet/ops/dcn/deform_conv_cuda.cpython-36m-x86_64-linux-gnu.so: undefined symbol: _ZN6caffe26detail37_typeMetaDataInstance_preallocated_32E HOT 1
- RuntimeError: Expected cudaMemcpy(&mask_host[0], mask_dev, sizeof(unsigned long long) * boxes_num * col_blocks, cudaMemcpyDeviceToHost) == cudaSuccess to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.) HOT 1
- Training citypersons with all the instances? HOT 1
- Welcome update to OpenMMLab 2.0
- Runtime error - Training with EuroCity persons HOT 1
- Training Caltech using CSP HOT 1
- What's ignore_other_vru in ECP evaluation? HOT 2
- result has no bbox HOT 5
- mmdet/ops/roi_align/src/roi_align_kernel.cu(145): error: identifier "THCudaCheck" is undefined HOT 1
- Update instruction for 2023 HOT 1
- custom dataset test HOT 1
- Evaluate different pre-trained model
- A request for advice on pedestrian tracking work. HOT 2
- a bug when converting crowdhuman to coco format
- Testing a CP trained model on ECP val dataset HOT 3
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